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Intelligently helping the human planner in industrial process planning

  • A. Famili (a1) and P. Turney (a1)

The function of a process planning system is to determine the methods by which a product is to be manufactured economically and competitively. In a modern manufacturing environment, a process planning system consists of highly trained people and complex software. The plans prepared by a process planning system are not always executed as planned. It is useful if the system can discover why plans fail, when they do fail. In order to learn why plans fail, the system must analyse a number of plans, both successful and unsuccessful, to find patterns in the failures of plans. This type of analysis is difficult for people, who are much better at analysing single events than multiple events.

The aim of the project described here is to design and implement a computer program which will help human planners in a process planning system to understand why plans fail. To achieve this aim, a program called IMAFO (Intelligent MAnufacturing FOreman) has been developed. IMAFO uses decision tree induction to analyse examples of both successful and unsuccessful plans.

The difficulties presented by this application are discussed and solutions are presented. Problems addressed include finding an appropriate set of attributes for describing the plans, using data efficiently, consolidating input from distinct sources, and presenting decision trees in an understandable form. Potential applications and directions for future research are considered.

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J. G. Carbonell 1989. Introduction: paradigms for machine learning. Artificial Intelligence 40, 19.

N. S. Flann and T. G. Dietterich 1989. A study of explanation-based methods for inductive learning. Machine Learning 4(2), 187226.

R. S. Michalski 1986. Understanding of the nature of learning: issues and research directions. In J. G. Carbonell , R. S. Michalski & T. M. Mitchell , eds, Machine Learning: An Artificial Intelligence Approach, Vol. II, Los Altos, CA: Morgan Kaufmann.

J. Mingers 1987. Expert systems—rule induction with statistical data. Journal of the Operational Research Society 38(1), 3947.

J. Mingers 1989 a. An empirical comparison of selection measures for decision-tree induction. Machine Learning 3(4), 319342.

J. Mingers 1989 b. An empirical comparison of pruning methods for decision tree induction. Machine Learning 4(2), 227243.

J. R. Quinlan 1983. Learning efficient classification procedures and their application to chess end games. In J. G. Carbonell , R. S. Michalski and T. M. Mitchell , eds. Machine Learning: An Artificial Intelligence Approach, Vol. I, pp. 463482. Los Altos, CA: Morgan Kaufmann.

J. R. Quinlan 1986 b. Induction of decision trees. Machine Learning 1(1), 81106.

J. R. Quinlan 1987 a. Simplifying decision trees. International Journal of Man-Machine Studies, 27, 221234.

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  • ISSN: 0890-0604
  • EISSN: 1469-1760
  • URL: /core/journals/ai-edam
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